Nevus-Associated Melanoma: Patient Phenotype and Potential Biological Implications

Review: The Key Factors to Melanomagenesis

Melanoma is the most dangerous form of skin cancer that develops from the malignant transformation of the melanocytes located in the basal layer of the epidermis (cutaneous melanoma). Melanocytes may also be found in the meninges, eyes, ears, gastrointestinal tract, genito-urinary system, or other mucosal surfaces (mucosal melanoma). Melanoma is caused by an uncontrolled proliferation of melanocytes, that at first may form a benign lesion (nevogenesis), but in time, it may transition to melanoma, determining what it is named, melanomagenesis. Some tumors may appear spontaneously (de novo melanoma) or on preexisting lesions (nevus-associated melanoma). The exact cause of melanoma may not be fully understood yet, but there are some factors that initiate and promote this malignant process. This study aims to provide a summary of the latest articles regarding the key factors that may lead to melanomagenesis. The secondary objectives are to reveal the relationship between nevi and melanoma, to understand the cause of "de novo" and "nevus-associated melanoma" and highlight the differences between these subtypes.

Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model

OBJECTIVE

The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the performance of such a model in achieving clinical-grade diagnoses of Spitz nevi, dermal and junctional melanocytic nevi, and melanomas.

METHODS

We created a beginner-level training environment by teaching our algorithm to perform cytologic inferences on 136,216 manually annotated tiles of hematoxylin and eosin-stained slides consisting of unequivocal melanocytic nevi, Spitz nevi, and invasive melanoma cases. We sequentially trained and tested our network to provide a final diagnosis-classification on 39 cases in total. Positive predictive value (precision) and sensitivity (recall) were used to measure our performance.

RESULTS

The tile-classification algorithm predicted the 136,216 irrelevant, melanoma, melanocytic nevi, and Spitz nevi tiles at sensitivities of 96%, 93%, 94% and 73%, respectively. The final trained model was able to correctly classify and predict the correct diagnosis in 85.7% of unseen cases (n = 28), reporting at or near screening-level performances for precision and recall of melanoma (76.2%, 100.0%), melanocytic nevi (100.0%, 75.0%), and Spitz nevi (100.0%, 75.0%).

CONCLUSIONS

Our pilot study proves that convolutional networks trained on cellular morphology to classify melanocytic proliferations can be used as a powerful tool to assist pathologists in screening for melanoma versus other benign lesions.

Early Melanoma Diagnosis With Sequential Dermoscopic Images

Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis are developed using single time-point images of lesions. Ignoring the temporal, morphological changes of lesions can lead to misdiagnosis in borderline cases. In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images. To this end, we construct our method in three steps. First, we align sequential dermoscopic images of skin lesions using estimated Euclidean transformations, extract the lesion growth region by computing image differences among the consecutive images, and then propose a spatio-temporal network to capture the dermoscopic changes from aligned lesion images and the corresponding difference images. Finally, we develop an early diagnosis module to compute probability scores of malignancy for lesion images over time. We collected 179 serial dermoscopic imaging data from 122 patients to verify our method. Extensive experiments show that the proposed model outperforms other commonly used sequence models. We also compared the diagnostic results of our model with those of seven experienced dermatologists and five registrars. Our model achieved higher diagnostic accuracy than clinicians (63.69% vs. 54.33%, respectively) and provided an earlier diagnosis of melanoma (60.7% vs. 32.7% of melanoma correctly diagnosed on the first follow-up images). These results demonstrate that our model can be used to identify melanocytic lesions that are at high-risk of malignant transformation earlier in the disease process and thereby redefine what is possible in the early detection of melanoma.

Nevus-associated melanoma: facts and controversies

Nevus-associated melanoma (NAM) is defined on histopathological basis by the coexistence of melanoma and nevus components. Melanomas developing on pre-existing congenital or acquired nevi are usually of the superficial spreading subtype and harbor the BRAFV600E mutation. NAM accounts for almost one-third of melanoma cases As compared to de novo melanoma, NAM develops on younger patients, is more frequently located on the trunk, and is associated with a high nevus count, light eye color and history of frequent sunburns. NAM has been regarded as a model to investigate melanoma origin. Molecular analysis defining the mutation profile of NAM's nevus and melanoma components supported the existence of two pathways of melanoma development, the first not involving clinically visible precursors, the second involving melanocytic nevi as precursors. Concerning diagnosis, dermatoscopy may identify nevus and melanoma components when located side-by-side, but no specific criteria have been described when superimposed. In-vivo reflectance confocal microscopy significantly enhances the recognition of NAM by allowing the detection of nevus remnants when superficially located. Regarding prognosis, NAM is generally thinner and more frequently in-situ than de-novo melanoma. Furthermore, studies reporting survival analysis demonstrated a trend towards better overall, distant-metastasis-free and recurrence-free survival. Although a clinical, phenotypic and molecular profile of NAM has been defined, controversies still exist. In the current review, we widely report and discuss facts and controversies on NAM.

Overdiagnosis of melanoma - causes, consequences and solutions

Malignant melanoma is the skin tumor that causes most deaths in Germany. At an early stage, melanoma is well treatable, so early detection is essential. However, the skin cancer screening program in Germany has been criticized because although melanomas have been diagnosed more frequently since introduction of the program, the mortality from malignant melanoma has not decreased. This indicates that the observed increase in melanoma diagnoses be due to overdiagnosis, i.e. to the detection of lesions that would never have created serious health problems for the patients. One of the reasons is the challenging distinction between some benign and malignant lesions. In addition, there may be lesions that are biologically equivocal, and other lesions that are classified as malignant according to current criteria, but that grow so slowly that they would never have posed a threat to patient's life. So far, these "indolent" melanomas cannot be identified reliably due to a lack of biomarkers. Moreover, the likelihood that an in-situ melanoma will progress to an invasive tumor still cannot be determined with any certainty. When benign lesions are diagnosed as melanoma, the consequences are unnecessary psychological and physical stress for the affected patients and incurred therapy costs. Vice versa, underdiagnoses in the sense of overlooked melanomas can adversely affect patients' prognoses and may necessitate more intense therapies. Novel diagnostic options could reduce the number of over- and underdiagnoses and contribute to more objective diagnoses in borderline cases. One strategy that has yielded promising results in pilot studies is the use of artificial intelligence-based diagnostic tools. However, these applications still await translation into clinical and pathological routine.

Dark pigmented lesions: Diagnostic accuracy of dermoscopy and reflectance confocal microscopy in a tertiary referral center for skin cancer diagnosis

BACKGROUND

There is lack of studies on the diagnostic accuracy of dermoscopy and reflectance confocal microscopy (RCM) for dark pigmented lesions.

OBJECTIVE

To assess the diagnostic accuracy of dermoscopy plus confocal microscopy for melanoma diagnosis of dark pigmented lesions in real life.

METHODS

Prospective analysis of difficult dark lesions with clinical/dermoscopic suspicion of melanoma referred for RCM for further analysis. The outcome could be excision or dermoscopic digital follow-up.

RESULTS

We included 370 clinically dark lesions from 350 patients (median age, 45 y). Because of the clinical/dermoscopic/RCM approach, we saved 129 of 213 unnecessary biopsies (specificity of 60.6%), with a sensitivity of 98.1% (154/157). The number needed to excise with the addition of RCM was 1.5 for melanoma diagnosis.

LIMITATIONS

Single institution based; Italian population only.

CONCLUSIONS

This study showed that RCM coupled with dermoscopy increases the specificity for diagnosing melanoma, and it helps correctly identify benign lesions. Our findings provide the basis for subsequent prospective studies on melanocytic neoplasms belonging to patients in different countries.

Digital dermoscopic changes during follow-up of de-novo and nevus-associated melanoma: a cohort study

BACKGROUND

Nevus-associated melanoma (NAM) has been regarded as a distinct biological entity from de-novo melanoma (DNM); however, static dermoscopy often fails in differentiating these entities. Digital dermoscopic monitoring allows to identify dynamic changes occurring during follow-up; this may improve diagnostic accuracy and potentially our knowledge on NAM biology. We aimed to define main independent factors associated with NAM diagnosis and those influencing follow-up time in a population of melanomas excised at follow-up.

METHODS

A cohort of melanomas excised at follow-up was retrospectively and consecutively selected. NAMs and DNMs were compared according to baseline features and main dermoscopic changes occurring during follow-up. Univariate and multivariable logistic and Cox's regression analysis were performed to respectively define factors associated with NAM diagnosis and those influencing the risk for excision.

RESULTS

Eighty-six melanomas were enrolled, of which 21 (24.4%) were nevus-associated. During follow-up NAMs mainly underwent atypical network modifications (47.6%), followed by inverse network (28.6%) and dermoscopic island (23.8%) worsening or appearance. DNMs were also mainly characterized by atypical network modifications (47.7%), however, a significant proportion of cases underwent irregular pigmentation/dots/globules or regression changes (29.2%), which were rarely seen among NAMs. Furthermore, both multivariable logistic and Cox's regression analysis demonstrated a significant association between NAM and a longer follow-up.

CONCLUSIONS

We demonstrated that among melanomas excised at follow-up, different patterns of dermoscopic changes may be found between NAMs and DNMs. This finding, together with the association of NAM with a longer follow-up time, supports the hypothesis of different biological behavior of these two entities.